Abstract
Critical to the understanding of the genetic basis for complex diseases is the modeling of human variation. Most of this variation can be characterized by single nucleotide polymorphisms (SNPs) which are mutations at a single nucleotide position. To characterize an individual’s variation, we must determine an individual’s haplotype or which nucleotide base occurs at each position of these common SNPs for each chromosome. In this paper, we present results for a highly accurate method for haplotype resolution from genotype data. Our method leverages a new insight into the underlying structure of haplotypes which shows that SNPs are organized in highly correlated “blocks”. The majority of individuals have one of about four common haplotypes in each block. Our method partitions the SNPs into blocks and for each block, we predict the common haplotypes and each individual’s haplotype. We evaluate our method over biological data. Our method predicts the common haplotypes perfectly and has a very low error rate (0.47%) when taking into account the predictions for the uncommon haplotypes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Halperin, E. (2004). Large Scale Recovery of Haplotypes from Genotype Data Using Imperfect Phylogeny. In: Istrail, S., Waterman, M., Clark, A. (eds) Computational Methods for SNPs and Haplotype Inference. RSNPsH 2002. Lecture Notes in Computer Science(), vol 2983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24719-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-24719-7_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21249-2
Online ISBN: 978-3-540-24719-7
eBook Packages: Springer Book Archive